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Using Simulations to Compare Teammates Fairly
Race Analysis

Using Simulations to Compare Teammates Fairly

Teammate comparisons are the cleanest dataset in Formula 1—same team, same engineering group, broadly the same car concept. And yet they’re also one of the easiest analyses to get wrong, because the public-facing outputs (points, podiums, headline results) are a noisy mix of execution and randomness. If you want to compare teammates fairly, you don’t need a hotter take. You need a way to hold conditions constant and ask: when the world is equal, who extracts more? That’s exactly what simulations and calculators are for, and it’s why RaceMate treats teammate comparison as a modelling problem first—then a narrative.

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Why Simulators Love Some Tracks More Than Others
Race Analysis

Why Simulators Love Some Tracks More Than Others

Some tracks make a season feel “stable”: the quickest car tends to qualify well, control the race, and bank points with relatively low drama. Other tracks feel chaotic: traffic matters, pit timing becomes everything, and one small error can flip a weekend from P4 to P11. Simulators are sensitive to this difference because they’re not only modeling pace — they’re modeling how pace turns into track position, and how track position turns into points.

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Strategy Risk vs Points Gain: The Tradeoff Simulators Expose
Race Analysis

Strategy Risk vs Points Gain: The Tradeoff Simulators Expose

Aggressive strategy is one of the easiest places in Formula 1 to confuse volatility with value. When a bold undercut wins on Sunday, it’s tempting to treat that result as proof the call was “the fastest” or “the correct” one. But strategy is a decision under uncertainty: you don’t choose the outcome, you choose a probability distribution of outcomes. The practical question isn’t “can this win?”—it’s “does this improve our expected points enough to justify the extra downside risk?”

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